Sensing Mobility and Routine Locations through Mobile Phone and Crowdsourced Data: Analyzing Travel and Behavior during COVID-19

被引:4
作者
Rodrigues, Claudia [1 ]
Veloso, Marco [1 ,2 ]
Alves, Ana [1 ,3 ]
Bento, Carlos [1 ]
机构
[1] Univ Coimbra, Ctr Informat & Syst CISUC, P-3030290 Coimbra, Portugal
[2] Polytech Inst Coimbra, Escola Super Tecnol & Gestao Oliveira Hosp ESTGOH, P-3030199 Coimbra, Portugal
[3] Polytech Inst Coimbra, Inst Super Engn Coimbra ISEC, P-3030199 Coimbra, Portugal
关键词
COVID-19; mobile phone data; crowdsourced data; trajectory analysis; routine locations; clustering;
D O I
10.3390/ijgi12080308
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The COVID-19 pandemic affected many aspects of human mobility and resulted in unprecedented changes in population dynamics, including lifestyle and mobility. Recognizing the effects of the pandemic is crucial to understand changes and mitigate negative impacts. Spatial data on human activity, including mobile phone data, has the potential to provide movement patterns and identify regularly visited locations. Moreover, crowdsourced geospatial information can explain and characterize the regularly visited locations. The analysis of both mobility and routine locations in the same study has seldom been carried out using mobile phone data and linked to the effects of the pandemic. Therefore, in this article we study human mobility patterns within Portugal, using mobile phone and crowdsourced data to compare the population's mobility and routine locations after the pandemic's peak. We use clustering algorithms to identify citizens' stops and routine locations, at an antenna level, during the following months after the pandemic's first wave and the same period of the following year. Results based on two mobile phone datasets showed a significant difference in mobility in the two periods. Nevertheless, routine locations slightly differ.
引用
收藏
页数:23
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